STATISTICAL GENOMICS AND BIOINFORMATICS PLATFORM
SAMPLE DATA ANALYSIS REPORT
Yong Won Jin
2022-11-15
# Data wrangling
library(tidyverse)
# Plotting
library(cowplot)
library(plotly)
library(factoextra)source('/home/jin/OneDrive/SGB/projects/functions.R')Project description
Study summary
This is where I would write a summary about the study/project - i.e. how it came about, who is involved, research question, what types of data were delivered, etc.
Objectives
- List research objectives here
- You can use asterisk or dash to make a list
Data preparation
Load data
For the demo, we’ll just use the mtcars dataset.
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Plots and Figures
Output from plotting functions are included.
mtcars %>%
select(mpg, cyl, disp, hp) %>%
rownames_to_column(var='Model') %>%
pivot_longer(cols=-Model) %>%
ggplot(mapping=aes(x=Model,value)) +
geom_bar(stat='identity') +
facet_grid(name ~ ., scales='free') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
2D and 3D PCA plots
For .html output, you can include 3D interactive plots using
plotly package.
2D PCA
res.pca <- prcomp(mtcars)
fviz_pca_ind(res.pca,
col.ind=as.factor(mtcars$cyl),
addEllipses = TRUE,
legend.title='cyl',
repel=TRUE,
title='')
3D PCA
plotly_pca(res.pca, as.factor(mtcars$cyl))Appendix
Code
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knitr::opts_chunk$set(
echo = TRUE,
include = TRUE,
cache = FALSE,
dpi = 150,
cache.lazy = FALSE
)
# Data wrangling
library(tidyverse)
# Plotting
library(cowplot)
library(plotly)
library(factoextra)
source('/home/jin/OneDrive/SGB/projects/functions.R')
mtcars
mtcars %>%
select(mpg, cyl, disp, hp) %>%
rownames_to_column(var='Model') %>%
pivot_longer(cols=-Model) %>%
ggplot(mapping=aes(x=Model,value)) +
geom_bar(stat='identity') +
facet_grid(name ~ ., scales='free') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
res.pca <- prcomp(mtcars)
fviz_pca_ind(res.pca,
col.ind=as.factor(mtcars$cyl),
addEllipses = TRUE,
legend.title='cyl',
repel=TRUE,
title='')
plotly_pca(res.pca, as.factor(mtcars$cyl))
sessionInfo()R session info
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: CentOS Stream 9
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.15.so
##
## locale:
## [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
## [5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
## [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] factoextra_1.0.7 plotly_4.10.0 cowplot_1.1.1 forcats_0.5.1
## [5] stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
## [9] tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.3 sass_0.4.2 jsonlite_1.8.0
## [4] viridisLite_0.4.0 carData_3.0-5 modelr_0.1.8
## [7] bslib_0.4.0 assertthat_0.2.1 highr_0.9
## [10] googlesheets4_1.0.0 cellranger_1.1.0 yaml_2.3.5
## [13] ggrepel_0.9.1 pillar_1.8.0 backports_1.4.1
## [16] glue_1.6.2 digest_0.6.29 ggsignif_0.6.3
## [19] rvest_1.0.2 colorspace_2.0-3 htmltools_0.5.3
## [22] pkgconfig_2.0.3 broom_1.0.0 haven_2.5.0
## [25] scales_1.2.0 tzdb_0.3.0 googledrive_2.0.0
## [28] car_3.1-0 generics_0.1.3 farver_2.1.1
## [31] ellipsis_0.3.2 ggpubr_0.4.0 cachem_1.0.6
## [34] withr_2.5.0 lazyeval_0.2.2 cli_3.3.0
## [37] magrittr_2.0.3 crayon_1.5.1 readxl_1.4.0
## [40] evaluate_0.15 fs_1.5.2 fansi_1.0.3
## [43] rstatix_0.7.0 xml2_1.3.3 tools_4.1.3
## [46] data.table_1.14.2 hms_1.1.1 gargle_1.2.0
## [49] lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.1
## [52] compiler_4.1.3 jquerylib_0.1.4 rlang_1.0.4
## [55] grid_4.1.3 rstudioapi_0.13 htmlwidgets_1.5.4
## [58] crosstalk_1.2.0 labeling_0.4.2 rmarkdown_2.14
## [61] gtable_0.3.0 abind_1.4-5 DBI_1.1.3
## [64] R6_2.5.1 lubridate_1.8.0 knitr_1.39
## [67] fastmap_1.1.0 utf8_1.2.2 stringi_1.7.8
## [70] Rcpp_1.0.9 vctrs_0.4.1 dbplyr_2.2.1
## [73] tidyselect_1.1.2 xfun_0.31